Dust Radiative Effects Over Global Oceans

Using one year of moderate resolution imaging spectroradiometer (MODIS) and clouds and the Earth's radiant energy system (CERES) data, we provide a satellite-based assessment of top-of-atmosphere (TOA) cloud-free shortwave and longwave dust radiative effects over global oceans from the Terra satellite. Over global cloud-free oceans, the dust net radiative effect is -0.7 plusmn0.2 W middotm^-2, and the TOA dust shortwave radiative effect (SWRE) dominates the longwave radiative effect (LWRE). Globally, the annual mean dust contribution to the total MODIS level 2 aerosol optical thickness (AOT, at 550 nm) is about 30% with a dust SWRE of -0.7 plusmn0.2 W middotm^-2 and LWRE of 0.03 plusmn0.02 W middotm^-2. Averaged over all seasons, the cloud-free diurnal mean dust radiative efficiency is -33 plusmn5 W middotm^-2 middottau^-1, and there is a remarkable linear relationship between the CERES SWRE and the MODIS AOT. This is the first satellite-based assessment of dust net radiative effect over the global oceans and will serve as a useful constraint for numerical modeling analysis.     

The brightness temperature adjusted dust index: An improved approach to detect dust storms using MODIS imagery

Moderate Resolution Imaging Spectroradiometer (MODIS) imagery provides a good data source for timely and accurate monitoring of dust storms. However, effective MODIS-based dust indices are inadequate. In this study, we proposed an improved brightness temperature adjusted dust index (BADI) by integrating the brightness temperatures of three thermal infrared MODIS bands: band₂₀ (3.66–3.84 μm), band₃₁ (10.78–11.28 μm) and band₃₂ (11.77–12.27 μm). We used the BADI to monitor several representative dust storms over the Northeast Asia between 2000 and 2011. When compared to commonly used MODIS-based dust indices, such as the brightness temperature difference index in band₃₂ and band₃₁ (BTD_32-31) and the normalized difference dust index (NDDI), the BADI captured the spatial extent and density of dust storms more accurately. The BADI detected dust storm extent with an overall accuracy >90%, which was 7% and 29% higher than the results derived from BTD_32-31 and NDDI, respectively. The BADI also demonstrated good agreement with the density indicator of MODIS Deep Blue Aerosol Optical Depth (R² = 0.59, P < 0.01). We suggest that the BADI is an effective tool to monitor large-scale dust storms.     

An improved dark object method to retrieve 500 m-resolution AOT (Aerosol Optical Thickness) image from MODIS data: A case study in the Pearl River Delta area,China

This paper presents an improved Dark Dense Vegetation (DDV) method for retrieving 500 m-resolution aerosol optical depth (AOT) based on MOD04-C005 arithmetic with the Moderate Resolution Imaging Spectroradiometer (MODIS) from the National Aeronautics and Space Administration (NASA). The improvements include change of the movement pattern of retrieval window, selection of a more suitable aerosol type, and storage of the look-up table. The method is then applied to obtain the AOT over the Pearl River Delta region (PRD). By comparing the results with the co-temporal ground sunphotometer observations in 2010, the correlation coefficient is found to be 0.794 with RMSE 0.139 and their variations remain consistent. Contrasts between model values in 2008 and MODIS AOT products in the same date also reveal a high accuracy of the improved DDV method. We also performed sensitivity tests to analyze the impacts of several parameters on apparent reflectance at different bands, and the results show that apparent reflectance is much more sensitive to surface reflectance and AOT than to elevation.     

Satellite-measured atmospheric aerosol content in Korea: anthropogenic signals from decadal records

Spatiotemporal characteristics and anthropogenic signals of aerosol optical thickness (AOT) distributions over Korea are investigated in this study using AOT time-series data from the Moderate Resolution Imaging Spectroradiometer (MODIS). AOT observations, a quantitative measure of the atmospheric quality, had significant geographical variations during the study period (2000–2010). Comparing metro cities or counties with similar populations, western regions showed higher AOT values than eastern regions. Particular matter with a diameter < 10 µm (PM₁₀), including aeolian dust or yellow dust, is the primary component of atmospheric aerosols, and their transport into the region has shown a strong seasonal pattern with its peak from March to April and lows from July to September. These seasonal dust patterns, however, did not correspond well to temporal AOT records, which typically reached the maximum level in June nationwide. Rather, widespread regional fire events and humidity showed significant correlations with AOT time-series. This correlation rapidly increased as the range of fire occurrence was extended to the west as far as 115°E. The relative humidity also had a significant correlation with AOT during the month of June. In addition to urban emission of anthropogenic aerosols, regional biomass burning and secondary growth of hygroscopic aerosols are considered important contributors to the degradation of the atmospheric environment during the non-Asian Dust season over the Korean Peninsula.     

一种基于阴影像元的光学遥感大气校正方法

提出一种基于6S模型 阴影像元的大气校正方法,适用于有阴影像元存在的高空间分辨率光学遥感影像。该方法从阴影像元与非阴影像元的信号差异估算气溶胶光学厚度,与暗目标方法相比,此方法避免地表反射率的假定难题。以北京市密云县的IKONOS影像对方法进行验证。在气溶胶光学厚度的估算上,该方法的估算结果与MODIS气溶胶产品基本一致,而且其结果的稳定性明显好于暗目标法。在大气校正的结果方面,针对各类地物,比较大气校正前后的光谱与同类典型实测地物光谱,结果说明大气校正能够大大恢复各类地物光谱的典型特征,这将有利于地物的识别。最后通过比较大气校正前后的NDVI发现,大气校正能够明显增大高植被覆盖区与低植被覆盖区NDVI的差别,使植被信息更加突出。     

The cloud phase discrimination from a satellite

A new technique to identify mixed-phase clouds but also clouds with supercooled water droplets using satellite measurements is proposed. The technique is based on measurements of the backscattered solar light at wavelengths 1.55 and 1.67 /spl mu/m in combination with cloud brightness temperature measurements at 12 /spl mu/m. For the first time, the concept of the phase index-temperature correlation plot (the P-T diagram) is introduced in the cloud remote sensing. Retrievals of cloud temperature and cloud phase index are performed using data from the Advanced Along Track Scanning Radiometer (AATSR) and Scaning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) both onboard the Envisat platform.     

Deriving Marine-Boundary-Layer Lapse Rate from Collocated CALIPSO, MODIS, and AMSR-E Data to Study Global Low-Cloud Height Statistics

Global cloud-top height statistics of marine-boundary-layer clouds are derived from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 aerosol and cloud layer products. The boundary-layer lapse rate in the northeast region of the Pacific Ocean is investigated using sea surface temperature (SST) data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E), cloud-top temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and cloud-top height data from CALIPSO. Based on the lapse rate derived from the combined CALIPSO/MODIS/AMSR-E measurements, cloud-top heights in regions within CALIPSO tracks are derived from AMSR SST and MODIS cloud temperature to test the validity of this approach. For homogeneous low-level clouds, the results agree with the cloud-top height from the collocated CALIPSO cloud-top height measurements. These results suggest that the database of derived lapse rates from the combined measurements can be applied to study cloud-top height climate statistics using the MODIS and AMSR data when CALIPSO observations are not available.      

Multidate image analysis of forest degradation in Equatorial Africa

Abstract

In this paper we process diachronic SPOT satellite images acquired with different viewing angles in order to assess the aerosol optical thickness (AOT) spatial distribution over Brescia.

This urban area, located in Northern Italy, is subject to frequent visibility‐reducing smog episodes. AOT was calculated by using the Differential Textural Analysis (DTA) code on a satellite data series consisting by one pollution‐free and various pollution‐loaded SPOT images. The resulting maps showed the horizontal distribution of AOT with a resolution of 500 metres. These maps can be readily integrated with the results obtained by mesoscale modelling, ground measurements, and respond to local scale application requirements. Satellite AOT retrieval compared successfully with available AOT ground‐based measurements and with pollution measurements in the ambient air. In this study the use of diachronic multiangle SPOT imagery allowed us to analyse the effect of the viewing angle variation on AOT retrieval accuracy based on the contrast reduction method.     

Urban aerosol mapping over Athens using the differential textural analysis (DTA) algorithm on MERIS-ENVISAT data

Low and moderate spatial resolution satellite sensors (such as TOMS, AVHRR, SeaWiFS) have already shown their capability in tracking aerosols at a global scale. Sensors with moderate to high spatial resolution (such as MODIS and MERIS) seem also to be appropriate for aerosol retrieval at a regional scale. We investigated in this study the potential of MERIS-ENVISAT data to resolve the horizontal spatial distribution of aerosols over urban areas, such as the Athens metropolitan area, by using the differential textural analysis (DTA) code. The code was applied to a set of geo-corrected images to retrieve and map aerosol optical thickness (AOT) values relative to a reference image assumed to be clean of pollution with a homogeneous atmosphere. The comparison of satellite retrieved AOT against PM₁₀ data measured at ground level showed a high positive correlation particularly for the AOT values calculated using the 5th MERIS’ spectral band (R²=0.83). These first results suggest that the application of the DTA code on cloud free areas of MERIS images can be used to provide AOT related to air quality in this urban region. The accuracy of retrieved AOT mainly depends on the overall quality, the pollution cleanness and the atmospheric homogeneity of the reference image.     

正则化滤波在反演地表光谱反照率中的应用

利用有限的卫星观测数据,通过反演地物二向性反射分布函数(BRDF)模型提取复杂地表的真实信息,是一个不适定的地球科学反演问题。为了克服离散不适定性带来的困难,在分析引起模型解不稳定的原因和数据误差传递机制的基础上,使用构建滤波函数的方法实现BRDF模型的正则化约束反演。实验结果表明,正则化滤波算法与MODIS AMBRALS算法精度相当,完全适用于具有充足观测和稀疏观测情况下的地物参数反演。     

The impact of size variations in the ground instantaneous field of view of pixels on MODIS BRDF modelling

Bidirectional reflectance distribution functions (BRDF) seek to represent surface reflectance anisotropy resulting from surface physical structure and changes in a satellite sensor’s view and solar illumination angles. NASA’s MODerate resolution imaging spectroradiometer (MODIS) is a wide field of view sensor that generates observations over a large range of view angles. Based on MODIS observations, a BRDF product and several sub-products have been developed by MODIS science teams, i.e. the MCD43 product suite. Variations in pixels’ ground instantaneous field of view (GIFOV), i.e. the size of a pixel’s footprint on the ground, is a well known effect associated with wide field of view sensors such as MODIS, but is not specifically considered in the MODIS BRDF algorithm nor has research been undertaken into its effects on MODIS BRDF modelling. This paper introduces two metrics to examine the relationship between reflectance variations associated with changes in MODIS pixels’ GIFOV and the MODIS BRDF (MCD43) product. These metrics are applied to four different study areas and epochs across the Australian continent. The two metrics are shown to be well correlated (mean correlation coefficient of 0.81 for the four study areas); suggesting that variations in pixels’ GIFOV are a consistent, non-random source of variance in MODIS BRDF modelling. The results contained in this paper suggest that all downstream products which include MODIS BRDF processing in their derivation and results directly based on MODIS BRDF processing may need to be reassessed.     

Atmospheric correction to IRS-P6 AWiFS data and its validation with ground measurements: A study over the semi-arid region

In this study, we have implemented a fast atmospheric correction algorithm to IRS-P6 advanced wide field sensor (AWiFS) satellite data for retrieving surface reflectance under different atmospheric and surface conditions. The algorithm is based on MODIS climatology products and simplified use of Second Simulation of Satellite Signal in Solar Spectrum (6S) radiative transfer code. The algorithm requires information on aerosol optical depth (AOD) for correcting the satellite dataset. The atmospheric correction algorithm has been tested for IRS-P6 AWiFS False colour composites covering the International Crops Research Institute for the Semi-Arid Tropics Farm, Patancheru, Hyderabad, India, under varying atmospheric conditions. Ground measurements of surface reflectance representing different land use/land cover, i.e. red soil, chick pea, groundnut and pigeon pea crops were conducted to validate the algorithm. Terra MODIS AOD₅₅₀ validated with Microtops-II sun photometer–derived AOD₅₀₀ over the urban region of Hyderabad exhibited very good correlation of ～0.92, suggesting possible use of satellite-derived AOD for atmospheric correction.     

Mapping saltcedar (Tamarix ramosissima) and other riparian and agricultural vegetation in the Lower Colorado River region using multi-spectral Landsat TM imagery

Saltcedar (Tamarix ramosissima), an invasive shrub species, has successfully invaded large extents of several riparian zones in the western United States and northern Mexico. Mapping the distribution and abundance of saltcedar over these large areas through a multi-seasonal, cost-effective monitoring approach using satellite remote sensing is very essential. Ground truth surveys were conducted at 79 locations where the spectral reflectance measurements of vegetation, type of plant species, plant heights, soil samples and GPS co-ordinates were recorded. All the sampling was designed to coincide with the satellite overpass period. The Landsat TM colour-composite spectral ratio image (normalized difference vegetative index (NDVI), R _1,5 and R _1,7 as green, blue and red) can clearly identify and map the areas infested with saltcedar. The Landsat image analysis shows that these spectral ratios can be applied to multiple satellite overpasses for monitoring the seasonal progression of the saltcedar growth over time.     

MODIS数据水体识别指数的识别效果比较分析

在光谱分析的基础上,应用不同水体指数对MODIS数据进行水体信息识别,并对其应用性能进行比较分析。结果表明,混合水 体指数（CIWI）是较理想的水体识别指数。若以反射率计算,并以0为判别阈值,则该指数的提取常数C的最佳取值为-0.85。 就目前的研究成果来看,MODIS数据还不太适合用于小型水体的识别。     

Leaf area index retrieval using gap fractions obtained from high resolution satellite data: Comparisons of approaches,scales and atmospheric effects

This study is aimed at demonstrating the feasibility of the large scale LAI inversion algorithms using red and near infrared reflectance obtained from high resolution satellite imagery. Radiances in digital counts were obtained in 10 m resolution acquired on cloud free day of August 23, 2007, by the SPOT 5 high resolution geometric (HRG) instrument on mostly temperate hardwood forest located in the Great Lakes – St. Lawrence forest in Southern Quebec. Normalized difference vegetation index (NDVI), scaled difference vegetation index (SDVI) and modified soil-adjusted vegetation index (MSAVI) were applied to calculate gap fractions. LAI was inverted from the gap fraction using the common Beer–Lambert's law of light extinction under forest canopy. The robustness of the algorithm was evaluated using the ground-based LAI measurements and by applying the methods for the independently simulated reflectance data using PROSPECT + SAIL coupled radiative transfer models. Furthermore, the high resolution LAI was compared with MODIS LAI product. The effects of atmospheric corrections and scales were investigated for all of the LAI retrieval methods. NDVI was found to be not suitable index for large scale LAI inversion due to the sensitivity to scale and atmospheric effects. SDVI was virtually scale and atmospheric correction invariant. MSAVI was also scale invariant. Considering all sensitivity analysis, MSAVI performed best followed by SDVI for robust LAI inversion from high resolution imagery.     

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.     

多样地表和大气状况下的MODIS数据云检测

云覆盖作为天气和气候变化的一个重要因子,对地表-大气能量平衡和水循环有着重要的影响,因此,快速、准确地利用卫星遥感技术检测云覆盖具有重要的实用价值和科学意义。利用卫星遥感数据,尤其是常用的Moderate Resolution Imaging Spectroradiometer(MODIS)影像数据,因其具有较高的光谱和时间分辨率,以及2330 km扫描幅宽,为大范围实时、准确地进行云检测提供了可能。目前,基于MODIS数据发展了大量的云检测方法,但因地表类型的多样性和大气状况(如空气污染和沙尘事件等)的复杂性,目前已有的云检测方法,检测精度通常具有较大的不确定性,且针对不同地表和大气状况缺乏普适性,同时也缺乏对检测精度的定量化评估。因此,本文首先比较了常用的3种云检测算法,并基于前人经验提出了两种改进方法(方法4和方法5),首先区分出云和冰雹,摒弃了不稳定的亮温波段,两种算法均适用于复杂地表和大气状况的云检测算法。结果显示,方法5可以较好地应用于基于MODIS数据的云检测,总体精度达92.6±7%,改进了现有基于MODIS数据的云检测算法;方法4平均总体精度82.9±13%,虽然精度相对较低,但云残留少,适合作为对云敏感度高的研究工作的云检测方法。     

Development of an image based integrated method for determining and mapping aerosol optical thickness (AOT) over urban areas using the darkest pixel atmospheric correction method,RT equation and GIS: A case study of the Limassol area in Cyprus

This paper presents the development of an image-based integrated method for determining and mapping aerosol optical thickness (AOT). Using the radiative transfer (RT) equation, a methodology was developed to create a Geographical Information System (GIS) model that can visually display the AOT distribution over urban areas. In this paper, the model was applied to eleven Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) satellite images of Limassol, Cyprus during 2010 and 2011 to determine the AOT levels in Limassol Cyprus during satellite overpass. The study is innovative and unique in that the RT equation, satellite images, the darkest pixel (DP) method of atmospheric correction and GIS were integrated to derive AOT from satellite images and display the AOT distribution over an urban area without the input of any meteorological or atmospheric parameters. The accuracy of the algorithm was verified through statistical analysis by the strong agreement between the AOT values derived using the algorithm and the in situ AOT values from the ground-based sensors.     

Effect of dust storm on ocean color and snow parameters

The present study clearly shows the influence of dust storms on chlorophyll bloom in the offshore region of the Arabian Sea, with a time lag of few days, during the pre-monsoon season. Various satellite derived parameters over the Arabian Sea, Himalayan and Tibet snow covered regions show large changes due to the influence of dust storms. The MODIS snow albedo gives unreliable values under the influence of dust storms due to increase in the aerosol loading over these regions and snow albedo product must be used in combination with snow pixel counts during the dust storm season. A detailed study is required for the quantitative evaluation of dust storms on the chlorophyll blooms in the Arabian Sea region and on the snow parameters in the Himalayan region.     

基于MODIS数据的中国陆面制图：方法软件和数据产品

本文介绍一个自动处理MODIS 1B数据并生产覆盖全中国陆面产品的软件系统。该算法改进了LAI（MOD15）,土地覆被分类（MOD12）,云检测（MOD35）,陆面反射率（MOD09）和气溶胶（MOD04）产品。这些算法的输入数据都是本地获取的参数,能够有效降低其带来的不确定性。产生的部分新产品是NASA标准产品中没有的,包括森林火烧迹地和PAR。数据处理系统运行于中国科学院资源与环境数据中心。